{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "import socket\n",
    "import time\n",
    "import traceback\n",
    "import numpy as np\n",
    "from colorama import init\n",
    "from multiprocessing import Process, Pipe\n",
    "init()\n",
    "def get_files_to_read(base_path):\n",
    "    starting_file_index = -1\n",
    "    ending_file_index = -1\n",
    "    pointer = 0\n",
    "    while True:\n",
    "        es = os.path.exists(base_path+'mcom_buffer_%d____starting_session.txt'%pointer)\n",
    "        ee = os.path.exists(base_path+'mcom_buffer_%d.txt'%pointer)\n",
    "        if (not es) and (not ee): break\n",
    "        assert not (ee and es), ('?')\n",
    "        if es: starting_file_index = pointer; ending_file_index = pointer\n",
    "        if ee: ending_file_index = pointer\n",
    "        pointer += 1\n",
    "        assert pointer < 1e3\n",
    "    assert starting_file_index>=0 and ending_file_index>=0, ('查找日志失败:', base_path)\n",
    "\n",
    "    file_path = []\n",
    "    for i in range(starting_file_index, ending_file_index+1):\n",
    "        if i==starting_file_index: file_path.append(base_path+'mcom_buffer_%d____starting_session.txt'%i)\n",
    "        else: file_path.append(base_path+'mcom_buffer_%d.txt'%i)\n",
    "        assert os.path.exists(file_path[0]), ('?')\n",
    "    return file_path\n",
    "\n",
    "def read_experiment(base_path):\n",
    "    files_to_read = get_files_to_read(base_path)\n",
    "    cmd_lines = []\n",
    "    for file in files_to_read:\n",
    "        f = open(file, 'r')\n",
    "        lines = f.readlines()\n",
    "        cmd_lines.extend(lines)\n",
    "    dictionary = {}\n",
    "\n",
    "    def rec(value,name): \n",
    "        if name not in dictionary:\n",
    "            dictionary[name] = []\n",
    "        dictionary[name].append(value)\n",
    "        return\n",
    "\n",
    "    for cmd_str in cmd_lines:\n",
    "        if '>>' in cmd_str:\n",
    "            cmd_str_ = cmd_str[2:].strip('\\n')\n",
    "            if not cmd_str_.startswith('rec('): continue\n",
    "            eval('%s'%cmd_str_)\n",
    "    return dictionary\n",
    "\n",
    "def stack_cutlong(arr_list, min_len=None):\n",
    "    if min_len is None:\n",
    "        min_len = min([len(item) for item in arr_list])\n",
    "    print([len(item) for item in arr_list],'\\tselect:', min_len)\n",
    "    return np.stack([item[:min_len] for item in arr_list])\n",
    "\n",
    "\n",
    "def smooth(data, sm=1):\n",
    "    if sm > 1:\n",
    "        y = np.ones(sm)*1.0/sm\n",
    "        d = np.convolve(y, data, 'valid')#\"same\")\n",
    "    else:\n",
    "        d = data\n",
    "    return np.array(d)\n",
    "\n",
    "\n",
    "def tsplot(ax, data, label, resize_x, smooth_sm=None, **kw):\n",
    "    if smooth_sm is not None:\n",
    "        print('警告 smooth_sm=',smooth_sm)\n",
    "        data = smooth(data, smooth_sm)\n",
    "\n",
    "    print('警告 resize_x=',resize_x)\n",
    "    x = np.arange(data.shape[1])\n",
    "    x = resize_x*x\n",
    "    est = np.mean(data, axis=0)\n",
    "    sd = np.std(data, axis=0)\n",
    "    cis = (est - sd, est + sd)\n",
    "    ax.fill_between(x,cis[0],cis[1],alpha=0.4, **kw)\n",
    "    ax.plot(x,est, linewidth=1.5, label=label, **kw)\n",
    "    ax.margins(x=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "party = [\n",
    "    # {\n",
    "    #     \"Method\": \"AddBnAddAe\",\n",
    "    #     \"path\": [\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-original-addbn-addae-r1\",\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-original-addbn-addae-r2\",\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-original-addbn-addae-r3\",\n",
    "    #     ]\n",
    "    # },\n",
    "\n",
    "# ZHECKPOINT/pymarl-starcraft-sim-AddBn-r1\n",
    "    # {\n",
    "    #     \"Method\": \"AddBn\",\n",
    "    #     \"path\": [\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-sim-AddBn-r1\",\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-sim-AddBn-r2\",\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-sim-AddBn-r3\",\n",
    "    #     ]\n",
    "    # },\n",
    "\n",
    "    # {\n",
    "    #     \"Method\": \"Original\",\n",
    "    #     \"path\": [\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-sim-original-r1\",\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-sim-original-r2\",\n",
    "    #         \"ZHECKPOINT/pymarl-starcraft-sim-original-r3\",\n",
    "    #     ]\n",
    "    # },\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    {\n",
    "        \"Method\": \"AddBn\",\n",
    "        \"path\": [\n",
    "            # \"ZHECKPOINT/pymarl-starcraft-5m-sim-AddBn-r1\",\n",
    "            \"ZHECKPOINT/pymarl-starcraft-5m-sim-AddBn-r2\",\n",
    "            \"ZHECKPOINT/pymarl-starcraft-5m-sim-AddBn-r3\",\n",
    "        ]\n",
    "    },\n",
    "    {\n",
    "        \"Method\": \"Original\",\n",
    "        \"path\": [\n",
    "            \"ZHECKPOINT/pymarl-starcraft-5m-sim-original-r1\",\n",
    "            \"ZHECKPOINT/pymarl-starcraft-5m-sim-original-r2\",\n",
    "            \"ZHECKPOINT/pymarl-starcraft-5m-sim-original-r3\",\n",
    "        ]\n",
    "    },\n",
    "\n",
    "]\n",
    "for ex in party:\n",
    "    for i, path in enumerate(ex['path']):\n",
    "        ex['path'][i] = '../' + ex['path'][i] + '/logger/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# smooth_sm = 4\n",
    "# main_key = 'reward'\n",
    "# main_key_name_on_graph = 'Mean Episode Rewards'\n",
    "# drop_data = 50 #None # 5\n",
    "\n",
    "\n",
    "smooth_sm = 1\n",
    "main_key = 'test-win-rate'\n",
    "main_key_name_on_graph = 'Test Win Rate'\n",
    "drop_data = 1 #None # 5\n",
    "\n",
    "max_raw_x = {\n",
    "}\n",
    "# drop_data = 50 #None # 5\n",
    "# drop_data = 500 #None # 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "readings of ../ZHECKPOINT/pymarl-starcraft-5m-sim-AddBn-r2/logger/\n",
      "readings of ../ZHECKPOINT/pymarl-starcraft-5m-sim-AddBn-r3/logger/\n",
      "readings of ../ZHECKPOINT/pymarl-starcraft-5m-sim-original-r1/logger/\n",
      "readings of ../ZHECKPOINT/pymarl-starcraft-5m-sim-original-r2/logger/\n",
      "readings of ../ZHECKPOINT/pymarl-starcraft-5m-sim-original-r3/logger/\n",
      "警告 平滑系数smooth_sm= 1\n",
      "警告 平滑系数smooth_sm= 1\n",
      "警告 平滑系数smooth_sm= 1\n",
      "警告 平滑系数smooth_sm= 1\n",
      "警告 平滑系数smooth_sm= 1\n"
     ]
    }
   ],
   "source": [
    "samples = []\n",
    "\n",
    "for ex in party:\n",
    "    pathes = ex['path']\n",
    "    for path in pathes:\n",
    "        ex['readings of %s'%path] = read_experiment(path)\n",
    "        print('readings of %s'%path)\n",
    "\n",
    "def shift_x(x):\n",
    "    return x* 8\n",
    "\n",
    "for ex in party:\n",
    "    # ex_ydata_batch = []\n",
    "    for path in ex['path']:\n",
    "        ydata = ex['readings of %s'%path][main_key]\n",
    "        ydata = np.array(ydata)\n",
    "        if smooth_sm is not None:\n",
    "            ydata = smooth(ydata, smooth_sm); print('警告 平滑系数smooth_sm=',smooth_sm)\n",
    "        for x, y in enumerate(ydata):\n",
    "            if (drop_data is not None) and (not x%drop_data==0): continue\n",
    "            if (ex['Method'] in max_raw_x) and (x > max_raw_x[ex['Method']]): continue\n",
    "            samples.append({\n",
    "                'Training Episodes':shift_x(x),\n",
    "                main_key_name_on_graph: y,\n",
    "                # 'color':party[exp_name]['color'],\n",
    "                'Method':ex['Method'],\n",
    "            })                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-505f44716fa5>:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xlabels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 515.323x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read finish!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "pd_sample = pd.DataFrame(samples)\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "# matplotlib.use('Agg')\n",
    "# import matplotlib.pyplot as plt;  plt.rc('font',family='Times New Roman');\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plt.ioff()\n",
    "#设置风格、尺度\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_context('paper', font_scale=2.5)\n",
    "# plt.rc({\n",
    "#     'text.latex.unicode': True,\n",
    "#     })\n",
    "sns.set(font=\"Times New Roman\", font_scale=1.88)\n",
    "# sns.set(font=\"Verdana\")\n",
    "res = sns.relplot(data=pd_sample, x='Training Episodes', y=main_key_name_on_graph, hue=\"Method\", kind=\"line\")\n",
    "# sns.lineplot(data=pd_sample, x='Training Episodes', y='Mean Episode Rewards', hue=\"Method\")\n",
    "for ax in res.fig.axes:\n",
    "    xlabels = ['{:,.0f}'.format(x) + 'k' for x in ax.get_xticks()/1000]\n",
    "    ax.set_xticklabels(xlabels)\n",
    "\n",
    "sns.move_legend(\n",
    "    res, \"center left\",\n",
    "    bbox_to_anchor=(0.79, 0.55)\n",
    ")\n",
    "# sns.move_legend(ax, \"upper right\", bbox_to_anchor=(1, 1))\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout(); \n",
    "# plt.savefig(\n",
    "#     './保存图像/IMG-1-1-new-seaborn_relplot.pdf',\n",
    "#     # bbox_inches='tight'\n",
    "#     )\n",
    "# plt.tight_layout(); \n",
    "plt.show()\n",
    "print('read finish!')"
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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